State and Local Coverage Changes Under Full Implementation of the Affordable Care Act

The Affordable Care Act (ACA) of 2010 includes a number of new policies intended to substantially reduce the number of people without health insurance. Key provisions to be implemented in 2014 include new health insurance exchanges, subsidies for coverage in those exchanges, health insurance market reforms, and an individual mandate. The ACA also includes an expansion of Medicaid coverage to individuals with incomes up to 138 percent of the Federal Poverty Level ($15,856 for an individual or $26,951 for family of three in 2013).¹  The Medicaid expansion under the ACA became a state option following the Supreme Court ruling in June of 2012.  At this point, it is not clear how many states will elect to expand Medicaid coverage.²  If all states were to do so, enrollment in Medicaid is projected to increase nationwide by about 18.1 million and the uninsured would decline by 23.1 million.[footnote Holahan2012]

This brief provides highlights from new state and sub-state estimates of how the number and composition of individuals enrolled in Medicaid/CHIP would change with full implementation of the ACA, including the Medicaid expansion. These estimates provide more detail on the projected coverage changes under the ACA at the state level than in prior research.³  They also provide new information on the expected coverage changes resulting from the ACA at the local level in all states. This analysis demonstrates that there is substantial variation across and within states in the magnitude and composition of the population that is projected to gain Medicaid coverage under the ACA. These estimates also provide guidance on the areas that are likely to experience the largest declines in the uninsured and where the residual uninsured are likely to be concentrated.

Methods

The analysis uses the Urban Institute’s American Community Survey – Health Insurance Policy Simulation Model (ACS-HIPSM).  This model simulates decisions of individuals in response to policy changes, such as Medicaid expansions, new health insurance options, subsidies for the purchase of health insurance, and insurance market reforms, using data from the American Community Survey (ACS). The estimates draw on a sample of approximately 7.5 million individuals from combined 2008, 2009, and 2010 ACS data. All three years of data were combined to achieve sufficient precision at both the state and local level.  The data was reweighted so that the distribution of the population by age, race, and sex in the pooled file matches 2011 population estimates published by the Census Bureau. For more detail on the ACS-HIPSM model and the methods underlying this analysis, see the Methods Appendix. For further information, see Documentation on the Urban Institute’s American Community Survey-Health Insurance Policy Simulation Model (ACS-HIPSM).

Medicaid Enrollment Increases Under the ACA

The demographic composition of Medicaid enrollees shifts under the ACA

Nationally, our model projects a 37.4 percent increase in Medicaid/CHIP enrollment under the ACA, with total enrollment rising from 48.3 million to 66.4 million.⁴   This enrollment includes both people newly-eligible for Medicaid coverage and also new enrollment among adults and children currently eligible for Medicaid coverage but not enrolled.  The composition of individuals gaining Medicaid enrollment is projected to differ from the current distribution of individuals covered by Medicaid/CHIP, primarily due to the increased coverage of nonelderly adults, particularly those without dependent children, who have historically been excluded from coverage.⁵  For example, 78.0 percent of new enrollees are adults, compared to 39.3 percent of current enrollees

. Children will represent a smaller share of Medicaid/CHIP beneficiaries than they currently do. Currently, children represent a majority of enrollees in 45 states, but after the ACA implementation, only 24 states will have more than half of their enrollees under the age of 19 (data not shown).

New Medicaid enrollees will also differ from current enrollees in terms of their race/ethnicity as well as language spoken at home. For example, 55.0 percent of new Medicaid/CHIP enrollees are white non-Hispanic, compared to 43.1 percent of current enrollees

. With full implementation of the ACA, the share of the Medicaid/CHIP population that would be living in a Spanish-speaking household is expected to decline.⁶  Specifically, 71.8 percent of new enrollees live in households in which all adults speak only English at home compared to 66.0 percent of current Medicaid/CHIP enrollees.

Medicaid enrollment increases across and within states under the ACA

Our model projects that with full implementation of the Medicaid expansion under the ACA, Medicaid enrollment increases will vary substantially across states.⁷  A total of 14 states are projected to experience enrollment increases in excess of 50 percent,⁸  while seven states are projected to expand their Medicaid/CHIP enrollment by less than 20 percent under the ACA

.⁹  The current differences among states in the expansiveness of Medicaid/CHIP eligibility for adults are reflected in the varying projected changes in Medicaid/ CHIP growth among adults. Overall, Medicaid/CHIP enrollment is expected to increase among adults by 74.1 percent, ranging from under 15 percent in New York and Vermont to over 150 percent in Montana, Nevada, and Idaho

The increases in Medicaid/CHIP enrollment projected under the ACA relative to current levels vary not only by state, but also across areas within states

. State boundaries can only account for approximately 60 percent of the total variation in Medicaid/CHIP enrollment growth seen across areas.¹⁰  In many states, areas of both high and low Medicaid/CHIP enrollment growth are found. California, where the median area in terms of Medicaid/CHIP enrollment growth is  38.5 percent, contains one local area with 111.2 percent projected growth in enrollment and another with 21.1 percent projected growth, which is below the national median

Outside of Massachusetts, Medicaid/CHIP enrollment is anticipated to increase in each area of the country under the ACA, particularly among adults who may not have been eligible in the past.  Nearly 40 percent of all local areas are projected to experience a doubling of their adult Medicaid/CHIP population, while 20 areas across the nation are expected to experience a tripling of their adult Medicaid/CHIP population under the ACA (data not shown).¹¹   Most of the areas with large projected increases in their adult Medicaid/CHIP population are in states that are expected to experience well above average enrollment increases in Medicaid/CHIP.

Medicaid demographic composition changes on the local level

There is also local area variation in the composition of Medicaid enrollment after ACA implementation. For example, the share in Spanish-speaking households varies substantially across local areas, and areas in seven different states are expected to have at least half of their post-ACA Medicaid enrollees in households in which the adults speak only Spanish [Arizona (3 areas), California (22 areas), Florida (4 areas), New Jersey (2 areas), New York (2 areas), and Texas (10 areas)]. These areas face an increase in the number of Medicaid enrollees with potential linguistic barriers to both enrollment and care. For some areas with a large share of the projected Medicaid population in Spanish speaking households after ACA implementation, this will represent a major increase in the number of Medicaid enrollees with potential linguistic barriers. For example, in the Kendall/ Kendale Lakes/ Tamiami area of Florida, the Medicaid population in Spanish speaking households would be projected to increase by 60.4 percent

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Impact of the ACA on the Uninsured

Projected declines in the uninsured across and within states

With full implementation of the ACA, the uninsured rate would decline by 47.1 percent nationally. Every state will experience a decrease in the uninsured of at least 25 percent, although it will vary as a consequence of different current uninsured rates and expected post-ACA uninsured rates.¹²  Uninsured rates currently vary from under 10 percent in Massachusetts, Hawaii, and the District of Columbia, to over 25 percent in Texas and Nevada. They will vary less after reform, with every state below 15 percent, and 7 states below 6 percent

.

The rates of uninsured also vary widely within certain states

. Currently, 264 of the 781 local areas have an uninsured rate of 20 percent or higher.  The ACA would greatly compress the distribution of uninsured rates across areas both within and between states

Characteristics of the uninsured under the ACA.  The composition of the uninsured is also expected to change under the ACA.  For example, proportionately fewer of the remaining uninsured will be between the ages of 19 and 24 nationwide.  Additionally, a higher proportion of the remaining uninsured will be Hispanic and in Spanish-speaking households

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Example: Projected Changes in Texas and Illinois

The data permit detailed estimates to be constructed for each state. As an example of how these estimates can be used to understand variation within states, we analyze results from two states, Texas and Illinois. Both states show wide variation in the effects of the ACA among local areas. Texas has 59 different local areas within the state, and Illinois has 29 local areas.

Texas

With full implementation of the Medicaid expansion in Texas, Medicaid/CHIP enrollment is projected to increase by 50.7 percent (from 3.9 million to 5.8 million), with 76.1 percent of the new enrollment occurring among adults ages 19 to 64. On average, the Texas Medicaid/CHIP population under full ACA implementation is projected to be more likely to be white non-Hispanic and to speak English relative to the pre-implementation Medicaid population

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In Texas, under full implementation of the ACA, Medicaid/CHIP growth rates are expected to vary between 34.1 and 97.5 percent across local areas

. The areas within Texas that would experience the largest growth in Medicaid/CHIP enrollment under the ACA include Collin, Randall, Fort Bend, and Brazos Counties, each in very different parts of the state.  Growth in Medicaid coverage of adults drives most growth in total enrollment in local areas with large increases.  Overall, Medicaid enrollment among nonelderly adults would grow by 147.5 percent in Texas under the ACA , with growth rates between 82.3 and 324.8 percent for areas within Texas

With full implementation of the ACA, the uninsured rate is projected to fall by 46.8% percent in Texas. Each of the 59 local areas within Texas are expected to see a decline of 40 percent or greater in their uninsured rate, and 22 areas would see their uninsured rate decline by more than 50 percent

. Fully 35.4 percent of the total expected decline in the uninsured would occur in the counties containing Texas’ three largest cities of Houston (Harris County), Dallas (Dallas County), and San Antonio (Bexar County), which together account for 1.0 million of the expected 2.9 million expected to gain coverage in Texas under the ACA (data not shown).  With ACA implementation that includes the expansion of Medicaid, the uninsured rate in the state of Texas would be 14.3 percent, higher than the expected national rate of 9.6 percent, but a marked decline compared to the pre-reform rate in Texas of 26.8 percent

Illinois

In Illinois, with full implementation of the ACA and the Medicaid expansion, Medicaid/CHIP enrollment is expected to increase by over 33 percent, or by about 696,000 new enrollees.  Among nonelderly adults 19-64, enrollment is expected to increase by 69.4 percent.  Total Medicaid enrollment post-ACA is estimated to be almost 2.8 million individuals, with the large majority of new enrollees expected to be white, non-Hispanic and adult. Unlike in Texas, the majority of new enrollees are projected to reside in households where everyone speaks English

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With full implementation of the ACA in Illinois, Medicaid/CHIP growth rates are expected to vary between 25.3 and 62.5 percent

. The area in Illinois with the largest projected increase in Medicaid/CHIP enrollment under the ACA is Champaign County. Other than McLean County in the central part of the state, the remaining top five areas are all part of the greater-Chicago region. More so than in Texas where, with several exceptions, most areas can anticipate a high share of post-reform Medicaid enrollees to come from Spanish speaking households, the language distribution in Illinois is much more varied across areas. Some areas, especially in the greater-Chicago region, are projected to have a high share of Medicaid enrollees in Spanish speaking households. The Cicero/ Berwyn/ Oak Park Area slightly west of Chicago would have almost half their Medicaid enrollees in such households, whereas 9 areas are projected to have less than 5 percent of their enrollees in such households

The uninsured rate in Illinois is projected to decrease 45.4 percent, or by about 814,000 individuals, with the full implementation of the ACA.  Every area is expected to experience a decrease in their uninsured rate greater than 35 percent. In over 10 areas, the decline is projected to exceed 50 percent. The post-reform rate of uninsured is not expected to be uniform across areas. Three areas within Chicago are expected to have uninsured rates persisting above 14 percent, whereas two counties in the central part of the state, Tazewell and McLean County, are projected to have rates below 5 percent

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Policy Implications

Health systems will need to prepare for coverage expansions and changes in the composition of the Medicaid population

We find substantial variation within states in the projected size of Medicaid enrollment gains under the ACA and in the composition of the population that would be newly covered by Medicaid/CHIP. Particular attention will be needed to assess whether Medicaid provider networks are sufficient to meet the needs of the new populations who would be served under the ACA.  Most of those gaining Medicaid are expected to be adults, whose service needs likely differ substantially from those of the children who currently predominate in many state Medicaid programs.

Our analysis suggests that health care networks in certain areas may face greater linguistic complexity with respect to the Medicaid/CHIP enrollees they would be serving under the ACA. Given that prior research has shown that language barriers can have a significant negative impact on access and use of care, it will be important to consider the geographic concentration of certain language groups in designing provider networks and services.¹³ 

Increases in Medicaid, as well as private insurance gains anticipated under the ACA, may put pressure on local health care systems to provide adequate access to care.¹⁴  Many of those newly insured under Medicaid may have primary care needs that had not been well addressed in the prior period when they were uninsured.¹⁵  Our estimates show substantial local variation in both the number of new Medicaid enrollees and their characteristics. With the growth in the Medicaid population that is expected under the ACA, it will be important to track the extent to which supply of services keeps pace with demand.  While the ACA includes provisions to address provider capacity within Medicaid, such as increased financing for federally qualified health centers and increases in primary care reimbursement rates, other policy changes may be needed to meet the health care needs of Medicaid enrollees.

Medicaid/CHIP Enrollment Gains have Potential to Expand Access to Care

Almost 73 percent of the new Medicaid coverage expected under the ACA draws from the ranks of the uninsured (data not shown).  Currently, these groups go without needed care at much higher rates than those who have Medicaid coverage.  Therefore, the acquisition of Medicaid coverage under the ACA should reduce the extent of unmet health needs and financial health burdens experienced by the low-income population and increase the extent to which they receive preventive and other types of needed care. However, the declines in the uninsured that are estimated here depend on full implementation of the ACA.  The states that choose not to expand Medicaid will experience much smaller increases in Medicaid enrollment and associated declines in the uninsured than reflected in these estimates, which will place greater demands on the safety net.  Moreover, this analysis shows that even with full implementation of the ACA, local areas in AZ, CA, FL, and TX can still expect to have one in every five people without health insurance coverage.  The adequacy of the safety net will remain an important policy concern, particularly in local areas where high rates of the uninsured persist.

Conclusion

Our ACS-based simulation projects that an additional 18.1 million would enroll in Medicaid/CHIP coverage under full implementation of the ACA, assuming all states expand Medicaid eligibility to 138 percent FPL. Our analysis provides new information on the extent to which these gains would vary across the country and show how the demographic and socioeconomic characteristics of the population covered by Medicaid/CHIP could change under the ACA.  This analysis also highlights the extent to which uninsured rates could decline across states and in all local areas.  Capacity and access issues will be important on the local levels as individuals who were previously uninsured now have coverage, and their needs may differ based on the changing demographic of enrollees.  Without full implementation of the ACA, many states and local areas will continue to see higher uninsured rates.

Data Sources

The American Community Survey. Pooled American Community Survey (ACS) data from 2008, 2009, and 2010 form the core data set for this model and the resultant estimates. The ACS is an annual survey fielded by the United States Census Bureau with a reported response rate of 98.0 percent in 2009.¹⁶  The estimates presented here are derived from the data that were collected from approximately 2.5 million non-elderly sample respondents (ages 0 to 64) in the civilian non-institutionalized population each year, yielding a total sample of approximately 7.5 million. The ACS is a mixed mode survey that includes households with and without telephones (landline or cellular.)  The ACS is designed to be state-representative, including samples from each county in the country.

Since 2008, the ACS has asked respondents about the health insurance coverage status at the time of the survey of each individual in the household. In an effort to correct for potential measurement errors in the ACS coverage data and to define coverage as including only comprehensive health insurance as opposed to single-service plans (e.g., dental coverage), we apply a set of logical coverage edits in the cases where other information collected in the ACS implies that coverage for a sample case likely has been misclassified.¹⁷  The edits target under-reported Medicaid/CHIP coverage among children and over-reported non-group coverage among both adults and children, which in turn, affect other coverage types. We draw from approaches that have been applied to other surveys¹⁸  and build on ACS edit rules used by the Census Bureau.¹⁹ 

American Community Survey-Health Insurance Policy Simulation Model. We use the Urban Institute’s American Community Survey – Health Insurance Policy Simulation Model (ACS-HIPSM) to estimate the effects of the ACA on the non-elderly at the state and local level.²⁰   The ACS-HIPSM model builds off of HIPSM, which uses the Current Population Survey (CPS) as its core data source, matched to several others, including the Medical Expenditure Panel Survey-Household Component (MEPS-HC). We apply the micro-simulation approach developed in HIPSM/CPS to model decisions of individuals in response to policy changes, such as Medicaid expansions, new health insurance options, subsidies for the purchase of health insurance, and insurance market reforms with data from the ACS. With the large ACS sample, we are able to produce more precise estimates for state and sub-state areas than available from models based on other data sources. Under our model, eligibility for Medicaid/CHIP and exchange subsidies are simulated using ACS data from 2008, 2009, and 2010 based on state-level eligibility guidelines for Medicaid and CHIP in 2010 and available information on the regulations for implementing the ACA.

We combine three years of ACS data to achieve sufficient precision at the state and local level. This process involves adjusting all dollar amounts such as income and wages to 2011 levels using the Consumer Price Index (CPI-U) and reweighting the combined file so that the distributions of demographic, employment, income, and health insurance coverage in the merged file match those of the 2011 ACS.

We simulate the main coverage provisions of the ACA as if they were fully implemented and the impacts were fully realized and compare the results to the model’s pre-reform baseline results. The HIPSM models use a micro-simulation approach based on the relative desirability of the health insurance options available to each individual and family under reform, taking into account a number of factors such as premiums and out-of-pocket health care costs for available insurance products, health care risk, whether or not the individual mandate would apply to them, and family disposable income.

Medicaid/CHIP Eligibility Simulation Model. We use The Urban Institute Health Policy Center’s ACS Medicaid/CHIP Eligibility Simulation Model to simulate pre-ACA eligibility for Medicaid/CHIP by comparing family income and other characteristics to the Medicaid and CHIP rules in each sample person’s state of residence.²¹  The model uses available information on eligibility guidelines, including income thresholds for the appropriate family size,²²  asset tests, parent/family status, and the amount and extent of income disregards for each program and state in place as of the middle of each year.²³  The model takes into account disregards for child care expenses, work expenses, and earnings in determining eligibility, but does not take into account child support disregards. For non-citizens, the model also takes into account length of U.S. residency in states where term of residency is a factor in eligibility.²⁴  Because the ACS does not contain sufficient information to determine whether an individual is an authorized immigrant and therefore potentially eligible for Medicaid/CHIP coverage, we impute documentation status for non-citizens based on a model developed using CPS ASEC data.²⁵ 

Estimates from our ACS models of pre-ACA eligibility have been extensively benchmarked to assess their validity and have been found to line up with those from other sources; for instance, despite the differences between the ACS and the CPS ASEC, the models from the two surveys produce fairly comparable results in terms of participation rates and the number of uninsured children who are eligible for Medicaid/CHIP but not enrolled for the same time frame.²⁶  The number and characteristics of individuals according to their eligibility for Medicaid/CHIP and their eligibility pathway (Medicaid vs. CHIP, etc.) are also quite similar across the two models.

Projections of Eligibility Under the ACA. Under the ACA, income eligibility will be based on the Internal Revenue Service tax definition of modified adjusted gross income (MAGI) and will include the following types of income for everyone who is not a tax-dependent child: wages, business income, retirement income, Social Security, investment income, alimony, unemployment compensation, and financial and educational assistance. The ACS asks only indirectly about unemployment compensation, alimony, and financial and educational assistance when it asks about “other income” so we imputed income from other sources using a model developed for the CPS which has more detail on income sources than the ACS.

To compute family income as a ratio of the poverty level, we sum the person-level MAGI across the tax unit.²⁷  In situations where a dependent child is away at school, the ACS does not contain data on the family income and other family information on the child’s record or the presence of the dependent child on the records of family members, so we assign some college students to families before beginning the simulation. Eligibility for Medicaid or subsidies under the ACA also depends on immigration status; HIPSM uses documentation status imputations described above.

We simulate ACA eligibility for adults and children for the eligibility pathways which correspond roughly to the order in which we expect eligibility to be determined. For children, we check for disability (SSI or Aged/Blind/Disabled eligibility under current rules), new Medicaid eligibility (family income up to 138 percent of FPL and meets immigration requirements), CHIP eligibility under current rules, and other eligibility under current rules, otherwise known as maintenance-of-eligibility. For adults, we check for disability (SSI or Aged/Blind/Disabled eligibility under current rules), Title IV-E/foster care, new Medicaid eligibility, and maintenance-of-eligibility.²⁸ 

We model subsidy eligibility, which depends on whether the family was offered affordable health insurance benefits, based on imputations of the presence of an insurance offer in the family and the value of the employee’s contribution towards the cost of the insurance premium among those with ESI. We impute offer status using regression models estimated from CPS data collected in 2005, the last year that the CPS included information on ESI offers in its February supplement. We first impute firm size on the ACS because offers are highly dependent on firm size. Similarly, we impute policyholder status to people in families with ESI because the ACS does not ask whose job offered the ESI.

Projections of Health Insurance Coverage Under the ACA. Once we have modeled eligibility status for Medicaid/CHIP and subsidized coverage in the exchanges, we use HIPSM to simulate the decisions of employers, families, and individuals to offer and enroll in health insurance coverage. To calculate the impacts of reform options, HIPSM uses a micro-simulation approach based on the relative desirability of the health insurance options available to each individual and family under reform.²⁹  The approach (known as a “utility-based framework”) allows new coverage options to be assessed without simply extrapolating from historical data, as in previous models. The health insurance coverage decisions of individuals and families in the model take into account a number of factors such as premiums and out-of-pocket health care costs for available insurance products, health care risk, whether or not the individual mandate would apply to them, and family disposable income. Our utility model takes into account people’s current choices as reported on the survey data. We use such preferences to customize individual utility functions so that their current choices score the highest, and this in turn affects behavior under the ACA. The resulting health insurance decisions made by individuals, families, and employers are calibrated to findings in the empirical economics literature, such as price elasticities for employer-sponsored and non-group coverage.

The first stage in the simulation process is to estimate additional enrollment in Medicaid and CHIP, both by those gaining eligibility under the ACA and those who are currently eligible, but not enrolled. Many characteristics are used to determine take-up, but the two most important are new eligible status and current insurance coverage, if any. The ACA includes a number of policies aimed at promoting enrollment, including a “no wrong door” enrollment policy whereby children and families will be screened and evaluated for Medicaid, CHIP, and subsidy eligibility no matter whether they apply for coverage (through Medicaid, CHIP or an exchange); new outreach funding; and procedures that minimize application and enrollment barriers. As a consequence, the model projects that Medicaid/CHIP participation rates will rise under the ACA for children and nonelderly adults who are eligible for Medicaid under current rules (see Holahan, Buettgens et al. 2012 for more on this issue.) While the HIPSM model projects that participation among children and non-elderly adults will increase with full implementation of the ACA, it projects that some individuals will remain uninsured despite being eligible for Medicaid/CHIP coverage. In subsequent stages, we model the following sequentially: enrollment in the non-group exchange, additional enrollment of the uninsured in employer-sponsored coverage, additional enrollment of the uninsured in non-group coverage outside of the exchange,  transitions from single to family ESI and transition from non-group to ESI.

Geographies Used for Local Estimates

The geographies used for this analysis are constructed from available county-level information and Super Public Use Microdata Area (SuperPUMA) definitions on the 2008, 2009, and 2010 pooled American Community Survey. The 531 SuperPUMAs are made up of combinations of the more than 2,000 PUMAs. PUMAs and SuperPUMAs have been defined by Census in conjunction with state and local governments to reflect areas that generally follow the boundaries of county groups, single counties, or census-defined “places,” constrained by the necessity to have a minimum population size (100,000 for PUMAs, 400,000 for SuperPUMAs). County of residence is available on the public-use files for residents of 374 counties, which together account for about 60% of the US population. Identifiable counties all have a population of at least 100,000, and include many of the nation’s largest counties, but do not include all such counties.

In defining local geographies, our methodology uses the county of residence to define a sub-state area unless the county is larger than one of its constituent SuperPUMAs, in which case the SuperPUMA is assigned as the geographic unit instead. When a SuperPUMA is partially composed of an identifiable county according to the rules above, a “Rest of SuperPUMA” area is assigned to individuals in the SuperPUMA who do not reside in the identifiable county. In five small states that are composed of just one SuperPUMA (AK, DC, SD, VT, and WY), we constructed two sub-state areas in each state based on the PUMA definitions for the state.

The result is that each individual is assigned to either a county or an “other area” which could be either: a full SuperPUMA, a “Rest of SuperPUMA,” or a specially constructed area. No resulting area is smaller than 100,000, and none is larger than the largest SuperPUMA of approximately 400,000 people. This yields 781 mutually exclusive geographies which span the entire US

. Of those, 316 are counties, and 465 are “other areas,” either full SuperPUMAs, “Rest of SuperPUMA areas”, or specially constructed areas.

Five states have just two areas, six have three areas, but over half have twelve or more areas

. The states with the largest number of local geographies are California, for which we have defined 78 sub-state areas using the above described methods, followed by Texas and Florida, with 59 and 48 sub-state areas, respectively. We assigned non-county geographies names based on the cities/towns/etc. that are located in the area. We also separately provide estimates for all 374 counties that are identifiable in the ACS.

Individual and Family Characteristics

The estimates that are available on kff.org/zooming-in-aca explore the composition of 1) individuals with Medicaid coverage/who were uninsured before implementation of the major coverage provisions of the ACA, 2) individuals who are projected to gain Medicaid under the ACA, and 3) individuals with Medicaid/who were uninsured after implementation of the ACA with respect to the following characteristics:

1. Age—Reported age of individual defined categorically (between 0-18, 19-24, 25-44, or 44-64).
2. Race—Reported race of individual. We define anyone who reported being “Hispanic” or “Latino” as Hispanic, and define single race-only for self-identified white or black respondents. Other ethnicities or those identifying multiple ethnicities are classified as “Other” race or ethnicity.
3. Gender—Reported gender of individual.
4. Language spoken at home—Reported language spoken at home by residents of the household aged 19 to 64. We define households where only English is spoken, only Spanish is spoken, English and some other language are spoken, or no English and not exclusively Spanish are spoken.

In this brief, we present estimates for all states; estimates are also presented for Texas and Illinois to spotlight the local variation in ACA impacts within a particular state.  We also provide estimates of the share of Medicaid/CHIP enrollees who live in Spanish-speaking households to highlight the variation in the demographic and socio-economic composition of enrollees within states. We report estimates for all geographies with sufficient sample size to provide reliable estimates along these socio-demographic dimensions. Additional dimensions were modeled for this population but the data was not published. Our sample size cutoff for estimate suppression was 150 respondents in that cell in the geographic area. Only estimates of those newly gaining Medicaid under reform (between 5 and 10 percent of all geographies) were suppressed by this rule.

 Limitations

Both the baseline and the ACA estimates presented here have a number of limitations, including measurement error in reported health insurance coverage on the ACS, which may not be fully addressed by the edits that were implemented and in the Medicaid and CHIP eligibility simulation model. Efforts to simulate eligibility for public coverage based on survey data are inherently challenging, particularly for adults. Challenges include misreporting of income, insurance coverage, or other information used to model eligibility and lack of specific information needed to simulate all the pathways to eligibility. The ACS, like many other surveys, does not contain information on such factors as pregnancy status, legal disability status,³⁰  child support amounts, whether custodial parents meet child support cooperation requirements, medical spending (which would be used to calculate spend-down for medically needy eligibility), and duration of Medicaid enrollment or income history to determine Transitional Medical Assistance (TMA) and related eligibility. Finally, there is additional uncertainty in any projection of ACA coverage impacts related to difficulties associated with predicting take up of different types of coverage under the ACA, federal and state actions that could number of implementation issues related to state and federal actions and guidance and a host of behavioral responses that are difficult to predict.

1. Based on the IRS tax definition of modified adjusted gross income (MAGI)—for more details on MAGI income definition, see: Buettgens, M., D. Resnick, V. Lynch, and C. Carroll. 2013.  Documentation on the Urban Institute’s American Community Survey Health Insurance Policy Microsimulation Model (ACS-HIPSM.)  The Urban Institute.  Washington DC. ↩︎
2. Sommers, B.D. and A.M. Epstein. 2010. “U.S. Governors and the Medicaid Expansion — No Quick Resolution in Sight.” New England Journal of Medicine 368(6): 496-499. ↩︎
3. Clemans-Cope, C., G. Kenney, M. Buettgens, C. Carroll, and F. Blavin. 2012. The Affordable Care Act’s Coverage Expansions Will Reduce Differences In Uninsurance Rates By Race And Ethnicity. Health Affairs, 31(5): 920-930; Holahan, Buettgens et al. 2012; Holahan, J. and I. Headen. 2010. “Medicaid Coverage and Spending in Health Reform: National and State-by-State Results for Adults at or Below 133% FPL.” Washington, DC: Kaiser Commission on Medicaid and the Uninsured.; Dorn, S. and M. Buettgens. 2011. “Net Effects of the Affordable Care Act on State Budgets” Washington, DC: The Urban Institute.  ↩︎
4. These national estimates are consistent with other models of Medicaid enrollment increases under the ACA: Blavin F., M. Buettgens, and J Roth. 2011. “State Progress Toward Health Reform Implementation: Slower Moving States Have Much to Gain.” Washington, DC: The Urban Institute; Holahan, Buettgens et al. 2012. ↩︎
5. While pathways through which childless adults can gain access to Medicaid coverage have existed, they’ve been limited to special categories of individuals and in most states income-based eligibility for childless adults has been very limited or nonexistent. ↩︎
6. Spanish-speaking households are defined as households in which all the non-elderly adults speak Spanish. ↩︎
7. This excludes Massachusetts, which we model as experiencing no change in Medicaid enrollment as a result of the ACA. ↩︎
8. Alaska, Colorado, Florida, Georgia, Idaho, Kansas, Montana, Nevada, North Dakota, Oregon, Texas, Utah, Virginia, Wyoming. ↩︎
9. Total includes Massachusetts. ↩︎
10. When we partition the total variation in area-level Medicaid/CHIP percent increases between within and across state variation, we find that variation within states accounts for 40.4 percent of the total. The rest (59.6 percent) is attributed to across state variance.  ↩︎
11. The areas with large percentage increases in their Medicaid/CHIP population do not correspond perfectly to the areas with the largest absolute Medicaid/CHIP population increases because of variation in reliance on Medicaid at baseline.  ↩︎
12. Except in Massachusetts, which, as indicated above, we model as exhibiting no change due to reform. ↩︎
13. Ponce, N., L. Ku, W. Cunningham, and R. Brown. 2006. Language Barriers to Health Care Access Among Medicare Beneficiaries. Inquiry, 43(1): 66-76. ↩︎
14. Holahan, Buettgens et al., 2012 ↩︎
15. Ku, L., K. Jones, P. Shin, B. Bruen, and K. Hayes. 2011. “The States’ Next Challenge — Securing Primary Care for Expanded Medicaid Populations.” New England Journal of Medicine, 364: 493-495. ↩︎
16. US Census Bureau. 2009. American Community Survey. ↩︎
17. Lynch V, and G. Kenney. 2011. “Improving the American Community Survey for Studying Health Insurance Reform.” Proceedings of the 10th Conference on Health Survey Research Methods, April 2011, Atlanta, GA. Hyattsville, MD.: Department of Health and Human Services; Lynch V., G. Kenney, J. Haley, and D. Resnick. 2011. Improving the Validity of the Medicaid/CHIP Estimates on the American Community Survey: The Role of Logical Coverage Edits. Submitted to the U.S. Census Bureau. ↩︎
18. National Center for Health Statistics, Division of Health Interview Statistics. 2005. 2004 National Health Interview Survey (NHIS) Public Use Data Release Survey Description. Hyattsville, MD: National Center for Health Statistics. ↩︎
19. Lynch V, M. Boudreaux, and M. Davern. 2010. “Applying and Evaluating Logical Coverage Edits to Health Insurance Coverage in the American Community Survey.” Suitland, MD.: U.S. Census Bureau, Housing and Household Economic Statistics Division. ↩︎
20. For a description of ACS-HIPSM, see: Buettgens, M., D. Resnick, V. Lynch, and C. Carroll. 2013.  Documentation on the Urban Institute’s American Community Survey Health Insurance Policy Microsimulation Model (ACS-HIPSM.)  The Urban Institute.  Washington DC. ↩︎
21. Kenney G., V. Lynch, A. Cook and, S. Phong. 2010. “Who And Where Are The Children Yet To Enroll In Medicaid And The Children’s Health Insurance Program?” Health Affairs 29(10):1920-1929.Kenney, G., M. Buettgens, J. Guyer, and M. Heberlein. 2011. “Improving Coverage For Children Under Health Reform Will Require Maintaining Current Eligibility Standards For Medicaid And CHIP.” Health Affairs, 30(12): 2371-2381; Kenney G., V. Lynch, J. Haley, M. Huntress, D. Resnick, and C. Coyer. 2011. “Gains for Children: Increased Participation in Medicaid and CHIP in 2009.” Washington, DC: The Urban Institute; Kenney G., V. Lynch, J. Haley, and M. Huntress. 2012. “Variation in Medicaid Eligibility and Participation among Adults: Implications for the Affordable Care Act.” Inquiry, 49(3): 231-253. ↩︎
22. Family-level characteristics used in determining pre-ACA eligibility, such as income, are based on the family groupings that states define during the process of determining eligibility under pre-ACA rules. However, indicators for “family” characteristics discussed in this paper refer to the family unit that is generally eligible for the same private plan, known as the health insurance unit (HIU). Eligibility for CHIP coverage is defined according to whether the child meets the income, asset, and documentation requirements for coverage and does not take into account whether the child might be subject to a waiting period. ↩︎
23. Cohen Ross, D., M. Jarlenski, S. Artiga, and C. Marks. 2009. “A Foundation for Health Reform: Findings of a 50 State Survey of Eligibility Rules, Enrollment and Renewal Procedures, and Cost- Sharing Practices in Medicaid and CHIP for Children and Parents During 2009.” Washington, D.C.: Kaiser Commission on Medicaid and the Uninsured; Heberlein et al., 2011, 2012; Kaiser Commission on Medicaid and the Uninsured. 2010. Expanding Medicaid to Low-Income Childless Adults under Health Reform: Key Lessons from State Experiences. Publication No. 8087. Washington, D.C.: Kaiser Commission on Medicaid and the Uninsured; Kaiser Commission on Medicaid and the Uninsured. 2011. Where are States Today? Medicaid and CHIP Eligibility Levels for Children and Non-Disabled Adults. Publication No. 7993-02. Washington, D.C.: Kaiser Commission on Medicaid and the Uninsured. ↩︎
24. National Immigration Law Center. 2011. Table: Medical Assistance Programs for Immigrants in Various States. ; Sullivan, J. 2010. “Expanding Coverage for Recent Immigrants: CHIPRA Gives States New Options.” Washington, DC: Families USA.; Heberlein, M., T. Brooks, J. Guyer, S. Artiga, and J. Stephens. 2011. Holding Steady, Looking Ahead: Annual Findings of a 50-State Survey of Eligibility Rules, Enrollment and Renewal Procedures, and Cost-Sharing Practices in Medicaid and CHIP, 2010–2011. Washington, D.C. Kaiser Commission on Medicaid and the Uninsured; Heberlein, M., T. Brooks, J. Guyer, S. Artiga, and J. Stephens. 2012. Performing Under Pressure: Annual Findings of a 50-State Survey of Eligibility, Enrollment, Renewal, and Cost-Sharing Policies in Medicaid and CHIP, 2011–2012. Washington, D.C.: Kaiser Commission on Medicaid and the Uninsured. ↩︎
25. Documentation status is imputed to immigrants in two stages using individual and family characteristics, based on an imputation methodology that was originally developed by Passel (Passel and Cohen, 2008). The approach is designed to produce imputations that match, in the aggregate, published summary estimates of the U.S. undocumented population, nationally and in a subset of large states.  ↩︎
26. Kenney, G., V. Lynch, A. Cook, and S. Phong. 2010b. Who And Where Are The Children Yet To Enroll In Medicaid And The Children’s Health Insurance Program? Health Affairs, 29(10): 1920-1929. ↩︎
27. We use “tax unit” and “HIU” or “health insurance unit” interchangeably in this report. ↩︎
28. Based on the most recent regulations as of this analysis, we assume maintenance-of-eligibility for children and for adults not above 138% FPL in an 1115 waiver or limited benefit program (federally- or state-funded programs that offer substantially more limited medical services, higher cost sharing, or other limitations). ↩︎
29. We apply this simulation approach to all individuals except those in Massachusetts, whom we assume will experience no change in health insurance status due to ACA implementation. ↩︎
30. States’ determinations of disability-related eligibility use additional criteria than the indicators of functional limitations available on the ACS. Thus, some of the sample people who appear in our model to be eligible through the disability pathway might not qualify when the more detailed information on their characteristics is taken into account. ↩︎